International Journal of Innovative Technology and ...€¦ · Principal, Department of Commerce...

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Transcript of International Journal of Innovative Technology and ...€¦ · Principal, Department of Commerce...

Page 1: International Journal of Innovative Technology and ...€¦ · Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania,
Page 2: International Journal of Innovative Technology and ...€¦ · Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania,

Editor-In-Chief Dr. Shiv Kumar

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE, Member of the Elsevier Advisory Panel

Blue Eyes Intelligence Engineering and Sciences Publication, Bhopal (MP), India

Associate Editor-In-Chief Chair Dr. Hitesh Kumar

Ph.D.(ME), M.E.(ME), B.E. (ME)

Professor and Head, Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal (MP), India

Dr. Anil Singh Yadav

Ph.D(ME), ME(ME), BE(ME)

Professor, Department of Mechanical Engineering, LNCT Group of Colleges, Bhopal (M.P.), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

Ph.D(CSE), MS(CSE), BSc(EE)

Department of Computer and Information Technology, Port Training Institute, Arab Academy for Science, Technology and Maritime

Transport, Egypt

Members of Associate Editor-In-Chief Chair Dr. Mayank Singh

PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT

Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Dr. Moinuddin Sarker

Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)

Stamford, USA.

Prof. (Dr.) Nishakant Ojha

Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India

Dr. Shanmugha Priya. Pon

Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East

Africa, Tanzania

Dr. Veronica Mc Gowan

Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman,

China.

Dr. Fadiya Samson Oluwaseun

Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern

Cyprus, Turkey.

Dr. Robert Brian Smith

International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India

Prof. MPS Chawla

Member of IEEE, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology &

Science Indore, Madhya Pradesh, India, Chairman, IEEE MP Sub-Section, India

Dr. Vinod Kumar Singh

Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India

Dr. Rachana Dubey

Ph.D.(CSE), MTech(CSE), B.E(CSE)

Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal

(M.P.), India

Executive Editor Chair Dr. Deepak Garg

Professor, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Page 3: International Journal of Innovative Technology and ...€¦ · Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania,

Members of Executive Editor Chair Dr. Vahid Nourani

Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah

Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

Dr. Xiaoguang Yue

Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.

Dr. Labib Francis Gergis Rofaiel

Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia

Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia

(Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg

Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan,

Saudi Arabia.

Technical Program Committee Chair Dr. Mohd. Nazri Ismail

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Members of Technical Program Committee Chair Dr. Haw Su Cheng

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas

Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

Dr. Gabil Adilov

Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.

Dr.Ch.V. Raghavendran

Professor, Department of Computer Science & Engineering, Ideal College of Arts and Sciences Kakinada (Andhra Pradesh), India.

Dr. Thanhtrung Dang

Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineering, HCMC University of Technology and Education,

Hochiminh, Vietnam.

Dr. Wilson Udo Udofia

Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.

Dr. Ch. Ravi Kumar

Dean and Professor, Department of Electronics and Communication Engineering, Prakasam Engineering College, Kandukur (Andhra

Pradesh), India.

Dr. Sanjay Pande MB

FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR)

Professor, Department of Computer Science and Engineering, G M Institute of Technology, Visvesvaraya Technological University

Belgaum (Karnataka), India.

Dr. Hany Elazab

Assistant Professor and Program Director, Faculty of Engineering, Department of Chemical Engineering, British University, Egypt.

Dr. M.Varatha Vijayan

Principal, Department of Mechanical Engineering, Mother Terasa College of Engineering and Technology, Pudukkottai (Tamil Nadu)

India.

Dr. S. Balamurugan

Director, Research and Development, Intelligent Research Consultancy Services (IRCS), Coimbatore (Tamil Nadu), India.

Dr. Rajalakshmi Rahul

FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR)

Founder and CEO Talaash Research Consultants, Chennai (Tamil Nadu), India.

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Editorial Chair Dr. Arun Murlidhar Ingle

Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar

(Maharashtra) India.

Members of Editorial Chair Dr. J. Gladson Maria Britto

Professor, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.

Dr. Wameedh Riyadh Abdul-Adheem

Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq

Dr. T. Sheela

Associate Professor, Department of Electronics and Communication Engineering, Vinayaka Mission’s Kirupananda Variyar

Engineering College, Periyaseeragapadi (Tamil Nadu), India

Dr. Manavalan Ilakkuvan

Veteran in Engineering Industry & Academics, Influence & Educator, Tamil University, Thanjavur, India

Dr. Shivanna S.

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. H. Ravi Kumar

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. Pratik Gite

Assistant Professor, Department of Computer Science and Engineering, Institute of Engineering and Science (IES-IPS), Indore (M.P),

India

Dr. S. Murugan

Professor, Department of Computer Science and Engineering, Alagappa University, Karaikudi (Tamil Nadu), India

Dr. S. Brilly Sangeetha

Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),

India

Dr. P. Malyadri

Professor, ICSSR Senior Fellow Centre for Economic and Social Studies (CESS) Begumpet, Hyderabad (Telangana), India

Dr. K. Prabha

Assistant Professor, Department of English, Kongu Arts and Science College, Coimbatore (Tamil Nadu), India

Dr. Liladhar R. Rewatkar

Assistant Professor, Department of Computer Science, Prerna College of Commerce, Nagpur (Maharashtra), India

Dr. Raja Praveen.N

Assistant Professor, Department of Computer Science and Engineering, Jain University, Bengaluru (Karnataka), India

Dr. Issa Atoum

Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education

University, Amman- Jordan

Dr. Balachander K

Assistant Professor, Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Pollachi

(Coimbatore), India

Dr. Sudhan M.B

Associate Professor & HOD, Department of Electronics and Communication Engineering, Vins Christian College of Engineering,

Anna University, (Tamilnadu), India

Dr. T. Velumani

Assistant Professor, Department of Computer Science, Kongu Arts and Science College, Erode (Tamilnadu), India

Dr. Subramanya.G.Bhagwath

Professor and Coordinator, Department of Computer Science & Engineering, Anjuman Institute of Technology & Management

Bhatkal (Karnataka), India

Dr. Mohan P. Thakre

Assistant Professor, Department of Electrical Engineering, K. K. Wagh Institute of Engineering Education & Research Hirabai

Haridas Vidyanagari, Amrutdham, Panchavati, Nashik (Maharashtra), India

Page 5: International Journal of Innovative Technology and ...€¦ · Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania,

Dr. P Venkata Subbareddy

Professor, Department of Computer Science and Engineering, Annamalai University (Tamil Nadu), India.

Dr. Muttipati Appala Srinuvasu

Professor, Department of Computer Science and Engineering, Gitam Deemed To Be University, Gandhi Nagar, Rushikonda

Visakhapatnam (Andhra Pradesh), India.

Dr. Namita Gupta

Professor, Department of Economics, MG Kashi Vidyapeeth, Varanasi (Uttar Pradesh), India.

Dr. Chandan Medatwal

Professor, Department of Management, University Of Kota, MBS Marg, Kota (Rajasthan), India.

Dr. Narasimhan D

Professor, Department of Mathematics, Srinivasa Ramanujan Centre Sastra Deemed University Kumbakonam (Tamil Nadu), India.

Dr. Yuriy Pyvovar

Professor, Department of Constitutional and Administrative Law, National Aviation University, Kiev, Ukraine.

Dr. Asim K. Mandal

Professor, Department of Agriculture, Bidhan Chandra Krishi Viswavidyalaya (BCKV), Mohanpur, Nadia (West Bengal), India.

Dr. Lokesh P Gagnani

Professor, Department of Computer Science and Engineering, C U Shah University, Nr. Kothariya Village, Dist. Surendranagar,

Wadhwan (Gujarat), India.

Dr. Trilochan Jena

Professor, Department of Civil Engineering, Siksha O Anusandhan (Deemed to be University), ITER, Bhubaneswar (Odisha), India.

Dr. S. Ismail Kalilulah

Professor, Department of Computer Science and Engineering, St. Peter’s Engineering College, Avadi, Chennai (Tamil Nadu), India.

Dr. S Vijayakumar

Professor, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.

Dr. Serhii Kozlovskyi

Professor, Department of Economics, Vasyl’ Stus Donetsk National University, Vinnytsia, Ukraine.

Dr. V. Jaiganesh

Professor, Department of Mechanical Engineering, Anna University Chennai (Tamil Nadu), India.

Dr. Mohankumar Namdeorao Bajad

Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat (Gujarat), India.

Dr. G. Purushotham

Professor, Department of Mechanical Engineering Sciences, Visvesvaraya Technological University, Belagavi (Karnataka), India.

Dr. Rajendiran Muthusamy

Professor, Department of Computer Science and Engineering, Sathyabama University, Chennai (Tamil Nadu), India.

Dr. S Madhava Reddy

Professor, Department of Mechanical Engineering, Osmania University, Hyderabad (Telangana), India.

Dr. Siddhartha Choubey

Professor, Department of Computer Science and Engineering, MATS University, Aarang, Raipur (Chhattisgarh), India.

Dr. Ebissa

Professor, Department of Civil Engineering, IIT Roorkee, Roorkee (Uttarakhand), India.

Dr. R. Dhanasekaran

Professor, Department of Mechanical Engineering, Anna University, Chennai (Tamil Nadu), India.

Dr. Kajal Chaudhary

Professor, Department of Commerce, Chaudhary Charan Singh University, Meerut (Uttar Pradesh), India.

Dr. Sivasankari

Assistant Professor, Department of Chemistry, Cauvery College for Women, Tiruchirappalli (Tamil Nadu), India.

Dr. K. S. Meenakshisundaram

Former Director, Cresent School of Business, Crescent University, Chennai (Tamil Nadu), India.

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S. No

Volume-8 Issue-4S3, March 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering and Sciences Publication

Page No.

1.

Authors: Ajay Sharma, Deepak Kholiya, Rakesh Chandra Bhadula, Amit Kumar Mishra, Neha Garg

Paper Title: Agriculture Transformation: A Method of Restricting Outmigration from Rural Hilly Region of

Uttarakhand, India

Abstract: Migration remained a prominent problem in rural hills of Uttarakhand. This trend has changed

socioeconomic and natural structure of the rural areas in hills of Uttarakhand. Rate of migration in rural hilly

areas remained high in last decades Major causes of migration from hills to plain are having lack of

infrastructure, low yield from farming, less employment opportunities etc. Despite of government policies and

subsidies for reverse migration, problem of migration still exists. This problem can be resolved by agriculture

transformation and exploring better opportunities of employment and income in the field of agriculture by

conversion of non-traditional method of agriculture towards Integrated farming systems.

Keywords: Migration, Agriculture transformation, entrepreneur, employment, Uttarakhand.

References:

1. Austine Eapen (2013),Uttarakhand Disaster-A Wake Up Call:A Case Study On Uttarakhand Disaster Response - 2013 Indian

Journal Of Public Administration Vol Lx. No. 2

2. Awasthi, I.C. (2012), Livelihood Diversities in Mountain Economy: Constraints and Opportunities, Concept Publishing Company

Pvt.Ltd., New Delhi.

3. Awasthi, I.C., Mehta, G. and Mamgain, R. (2014). Uttarakhand Disaster: Lessons and way

4. Bora, R.S. (1996), Himalayan Out-migration, Sage Publication, New Delhi.

5. Deshingkar, P. and D. Start (2003), Seasonal Migration for Livelihoods, Coping,Accumulation and Exclusion, Overseas

Development Institute 111 Westminster Bridge Road London SE1 7JD UK.

6. Deshingkar, Priya and Sven Grimm (2004),Voluntary Internal Migration: An Update, Paper Commissioned jointly by the Urban

and Rural Change Team and the Migration Team with in the Policy Division of the British Government’s Department for

International Development. Impact Assessment of Disaster and Trends in Sustaining Recovery in Uttarakhand.

7. http://www.uttarakhandpalayanayog.com/ Accessed 10 April 2019

8. https://economictimes.indiatimes.com/articleshow/64044151.cms?from=mdr&utm_source=contentofinterest&utm_medium=text

&utm_campaign=cppst Accessed 22 May 2019

9. https://economictimes.indiatimes.com/news/politics-and-nation/over-700-uttarakhand-villages-deserted-in-10-years-

report/articleshow/64044151.cms?from=mdr Accessed 12 June 2019

10. https://uk.gov.in/files/Uttarakhand_at_a_glance-final_2013-14.pdf Accessed 12 June 2019

11. Human Development Report of the State of Uttarakhand , Directorate Of Economics & Statistics Department of Planning Government Of Uttarakhand Institute For Human Development Plot No. 84, Fie, Patparganj, Delhi-110092 Website:

Ihdindia.Org 31 December 2018.

12. IDFC (2002), An Agenda for Development, Industrial Development Finance Company, Mumbai.

13. Khanka,S.S.(1988), Labour Force, Employmentand Unemploymentina Backward Economy. New Delhi:Himalaya Publishing

House.

14. Mamgain, Rajendra P.(2004), Employment, Migration and Livelihoods in The Hill Economy of Uttarakhand, Ph.D. Thesis

submitted to Jawaharlal Nehru University, New Delhi.

15. Nong Zhu, Xubei Luo (2010) The impact of migration on rural poverty and inequality: A case study in China, Agricultural

Economics pp 191–204 DOI: 10.1111/j.1574-0862.2009.00434.x

16. Pramod Kumar and Masaru Sasakiy (2018), Migration and natural disaster: Ex-ante preparedness and contribution to ex-post

community recovery, Migration Studies PP. 1–25 doi:10.1093/migration/mny006

17. Singh J.P(1992), Migration in India: A Review, Asian and Pacific Migration Journal, Vol. 1, No. 1

18. Srivastava, R.S. (2005), Internal Migration Links with Poverty and Development, Country Paper, presented at the Regional

Conference on Migration and Development in Asia, LanZhou, China, 14-16 March.

1-5

2.

Authors: Aman Kumar, Shakti Kundu, Santosh Kumar, Umesh Kumar Tiwari, Jasmeet Kalra

Paper Title: S-TVDS: Smart Traffic Violation Detection System for Indian Traffic Scenario

Abstract: The world's second-largest road network is in India, which is directly and similarly proportional to

the causes of road rules violations, accidents, and a large per-year death ratio. Now in semi-structured cities, it

becomes the biggest challenge to make people abide by traffic rules. Much different automation has been

proposed to automate and to make it happens in India. Many researchers are also trying to solve this with

computing technology advancement. As from the recent past, AI & ML not only making things smarter but also

have proven to a valuable technological human assistant of dealing with such issues with intelligence. In this

paper, we proposed a smart traffic violation detection system as a solution for the same issues in the Indian

scenario. The advanced and intelligent form of visual computing will assist in detection as well as pruning

actions /alerts accordingly with classification of types of violations.

Keywords: Ttraffic violation detection, smart traffic violation detection & alert system, AI traffic monitoring,

smart traffic management, smart traffic alert system.

References:

1. Road Accidents in India. Available: [https://morth.nic.in/road-accident-in-india]. Accessed: 2019-06-10. 2. A new red-light and speed enforcement system has been deployed in the Indian capital. Available:

[https://www.traffictechnologytoday.com/news/enforcement/indian-capital-deploys-new-speed-and-red-light-violation-detection-

6-10

Page 7: International Journal of Innovative Technology and ...€¦ · Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania,

system.html]. Accessed: 2019-03-01. 3. Traffic Violation Detection. Available: [https://www.hikvision.com/hk/solutions/solutions-by-industry/traffic/traffic-violation-

detection/]. Accessed: 2019-06-03.

4. Red Light Violation Detection System. Available: [http://www.onnyx.in/red-light-violation-detection-system.html]. Accessed: 2019-07-13.

5. No more traffic violations! (smart traffic signal system to keep a tab on violators). Available:

[https://india.smartcitiescouncil.com/article/no-more-traffic-violations-smart-traffic-signal-system-keep-tab-violators]. Accessed: 2017-11-29.

6. Smart cams to rein in traffic violations. Available: [https://www.newindianexpress.com/states/odisha/2018/dec/01/smart-cams-to-

rein-in-traffic-violations-1905822.html]. Accessed: 2018-12-01. 7. Intelligent Cameras for Traffic Management. Available: [https://www.trafficinfratech.com/intelligent-cameras-for-traffic-

management/]. Accessed: 2019-03-16.

8. The problem Smart Traffic System solves. Available: [https://devfolio.co/submissions/smart-traffic-system]. Accessed: 2019-06-01.

9. Red Light Violation Detection System. Available: [https://www.indiamart.com/proddetail/red-light-violation-detection-system-

11925985348.html]. Accessed: 2020-03-03. 10. Smart Traffic Signaling System To Catch Violators In Odisha Capital. Available: [https://sambadenglish.com/soon-smart-traffic-

signalling-system-to-catch-traffic-violators-in-odisha-capital/]. Accessed: 2019-06-03.

11. Pengfei Zhou, Zhiyuan Chen, and Mo Li. 2013. Smart traffic monitoring with participatory sensing. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys ’13). Association for Computing Machinery, New York,

NY, USA, Article 26, 1–2. DOI:https://doi.org/10.1145/2517351.2517379

12. Guoyu Ou, Yang Gao, and Ying Liu. 2012. Real-Time Vehicular Traffic Violation Detection in Traffic Monitoring Stream. In

Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent

Technology - Volume 03 (WI-IAT ’12). IEEE Computer Society, USA, 15–19. DOI:https://doi.org/10.1109/WI-IAT.2012.91

13. Mayank Singh Chauhan, Arshdeep Singh, Mansi Khemka, Arneish Prateek, and Rijurekha Sen. 2019. Embedded CNN based

vehicle classification and counting in non-laned road traffic. In Proceedings of the Tenth International Conference on Information

and Communication Technologies and Development (ICTD ’19). Association for Computing Machinery, New York, NY, USA,

Article 5, 1–11. DOI:https://doi.org/10.1145/3287098.3287118

14. Arnaud Prouzeau, Anastasia Bezerianos, and Olivier Chapuis. 2015. Road traffic monitoring on a wall display. In Proceedings of

the 27th Conference on l’Interaction Homme-Machine (IHM ’15). Association for Computing Machinery, New York, NY, USA,

Article 29, 1–6. DOI:https://doi.org/10.1145/2820619.2825009

15. Isabel Marti, Vicente R. Tomas, Arturo Saez, and Juan J. Martinez. 2009. A Rule-Based Multi-agent System for Road Traffic Management. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent

Agent Technology - Volume 03 (WI-IAT ’09). IEEE Computer Society, USA, 595–598. DOI:https://doi.org/10.1109/WI-

IAT.2009.358

16. Chunli Chen, Huifang Liu, and Zhenhua Wang. 2019. Analysis and Design of Urban Traffic Congestion in Urban Intelligent Transportation System Based on Big Data and Internet of Things. In Proceedings of the 2019 International Conference on

Artificial Intelligence and Computer Science (AICS 2019). Association for Computing Machinery, New York, NY, USA, 659–

665. DOI:https://doi.org/10.1145/3349341.3349486

17. 17. Daniyar Kurmankhojayev, Gulnur Tolebi, and Nurlan S. Dairbekov. 2019. Road traffic demand estimation and traffic signal

control. In Proceedings of the 5th International Conference on Engineering and MIS (ICEMIS ’19). Association for Computing

Machinery, New York, NY, USA, Article 2, 1–5. DOI:https://doi.org/10.1145/3330431.3330433

3.

Authors: Amit Kumar Mishra, Rakesh Chandra Bhadula, Neha Garg, Deepak Kholiya, V. N. Kala

Paper Title: Impact of Dataset Size and Performance Analysis of IDS using Random Forest Algorithm in ‘R’

Language

Abstract: With the advancement of new technologies in today’s era, Big Data has shown tremendous growth

and popularity. With this exaltation , Big data isn't simply presenting challenge as far as volume yet in addition

as far as its high speed generation. New data is fetched extremely fast so it becomes essential to deal with such

voluminous data. Machine Learning expedites computers in building models from input data so as to automate

decision-making processes. Machine learning algorithms such as ”Random Forest” is used with the help of

certain datasets to instruct and train computers and also train them to respond like human beings. Selecting an

appropriate dataset(size, parameters) plays an important role in providing efficient and effective result. In this

paper, an analytical approach is used for IDS i.e. “Intrusion Detection System “where “ Random Forest

algorithm” is used to analyze the training time by increasing the size of the dataset and observe the impact of

frequent changes(size) on various evaluation metrics .Finally performance analysis is carried out and It is

observed that the performance of IDS is better and more accurate .

Keywords: Intrusion Detection System, Data set, Evaluation metrics, Machine learning, Random Forest

References:

1. R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat and S. Venkatraman, "Deep Learning Approach for

Intelligent Intrusion Detection System," in IEEE Access, vol. 7, pp. 41525-41550, 2019.

2. Evaluation of machine learning algorithm for intrusion detection system,

“https://arvix.org/ftp/arxiv/papers/1801/1801.02330.pdf”

3. “http://www.daily.co.kr/news/article.html?no=157416”

4. Sally, Hassen and Sami Bourouis, “Intrusion Detection alert management for high-speed networks: Current research and

applications.” Security and Communication Networks 8.18(2015): 4362-4372.

5. Machine learning for Intrusion detection on public-datasets. https://ieeexplore.ieee.org/document/7726677

6. Review for data classification evaluations https://pdfs.semanticscholar.org/6174/3124c2a4b4e550731ac39508c7d18e520979.pdf

7. Neelam Singh, Neha Garg, Varsha Mittal,” Big Data – insights, motivation and challenges, in International Journal of Scientific

& Engineering Research, Volume 4, Issue 12, December-2013.

8. Yasir Hamid, M. Sugumaran and V. R. Balasaraswathi,”IDS Using Machine Learning - Current State of Art and Future

Directions”, British Journal of Applied Science & Technology 15(3): 1-22, 2016

9. Ahmad, M. Basheri, M. J. Iqbal and A. Raheem, "Performance comparison of support vector machine random forest and extreme

learning machine for intrusion detection", IEEE Access, vol. 6, pp. 33789-33795, 2018.

10. Wanda, Putra. ”A Survey of Intrusion Detection System.” International Journal of Informatics and Computation 1, no. 1 (2020):

11-14

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1-10.

11. Kumar G ,” Evaluation Metrics for Intrusion Detection Systems - A Study”,International Journal of Computer Science and Mobile Applications (IJCSMA) 2 (11): 11-17, 2014.

12. Cagatay Catal, Banu Diri, “Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault

prediction problem”, Information Sciences Volume 179, Issue 8, 29 March 2009, Pages 1040-1058. 13. Cutler A., Cutler D.R., Stevens J.R. (2012) Random Forests. In: Zhang C., Ma Y. (eds) Ensemble Machine Learning. Springer,

Boston, MA

4.

Authors: Shipra Gupta, Shipra Agarwal

Paper Title: Changing Trends in Indian Education System: Merits and Demerits

Abstract: There are a great changes come in Indian education system. In foreign countries, online education

systems are used as earlier but in India it was used only in professional studies. Now the scenario has changed.

Now India is using online education not only for professional studies, but also schools, higher studies and in

colleges also. In this manuscript this is trying to understand the various merits and demerits of using online

education system. In this study, the main focus is to understand the education pattern of the country over the

years and the role of government policies in the up gradation of Indian education system.

Keywords: Indian education system, government policies, higher studies.

References:

1. AppuSrva, 2017, Present Education System in India, http://www.groupdiscussionideas.in/present-education-system-in-india/,

28th January.

2. ASER, 2016, Annual Status of Education Report 2016, http://www.asercentre.org/p/289.html

3. Bittersweet, 2017, The collapse of education is the collapse of the Nation, https://www.jamiiforums.com/threads/the-collapse-ofeducation-is-the-collapse-of-the-nation.1231557/, April 7

4. Classbase, 2016, Education System in India, http://www.classbase.com/countries/India/Education-System

5. Indiatoday, 2017, Budget 2017: Education sector analysis, then and now, https://www.indiatoday.in/educationtoday/news/story/budget-2017-education-958329-2017-02-01, February 1.

6. Indrail, 2015, Higher education in India, https://indrailsearch.wordpress.com/2015/06/13/education-higher-education-in-india/,

June 13 7. Kremer, etc. (2004), "Teacher Absence in India: A Snapshot", ''Journal of the European Economic Association''.

http://globetrotter.berkeley.edu/macarthur/inequality/papers/KremerTeacherAbsenceinIndia.pdf.

8. Kumkum Joshi, 2017, How India's education system is breaking the country, http://www.dailyo.in/voices/school-education-englishgovernment-school/story/1/16610.html

9. MapsofIndia, 2016, Education in India, http://www.mapsofindia.com/education/, January 19

10. MHRD., 2016, Educational statistics at a glance, http://mhrd.gov.in/sites/upload_files/mhrd/files/statistics/ESG2016_0.pdf 11. Nanda, Prashant K. 2017, NIRF rankings amplify education inequality in India,

http://www.livemint.com/Education/jtktzCDnDYJ30EE2jHayLL/National-rankings-amplify-education-inequality-in-India.html

12. Offbeat Society, 2009, Education System of India: Its Functions, Drawbacks and Its Contribution, http://theviewspaper.net/education-system-of-india-its-functions-drawbacks-and-its-contribution/ 13th July.

13. Prayatna, 2014, Education in India: Past, Present and the Future. Ideas, Policies and Initiatives,

http://prayatna.typepad.com/education/datastatistics/, February 14. 14. Sanyal D. K., 2017, 14 facts about Indian education system and a remedy, https://sanyaldk.in/doing-it-in-open-source-way/14-

factsabout-indian-education-system-and-a-remedy/

15. Sasi Kumar V., 2016, The Education System in India, https://www.gnu.org/education/edu-system-india.en.html, 18th November. 16. Studylib, 2017, Private sector’s contribution to K-12 education in India - Current impact, challenges and way forward,

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17. Tarang, 2017, Facts & Statistics about Education in India, http://www.tarang.org/facts/facts-statistics-about-education-in-india2.html

18. World University Rankings 2017, https://www.timeshighereducation.com/world-university-

rankings/2017/worldanking#!/page/3/length/25/sort_by/rank/sort_order/asc/cols/stats 19. World Bank, 2015, Educating India’s Children, http://www.worldbank.org/en/country/india/brief/educating-india-children,

September 18

20. Zahoor Ahmad Lone. Impact of Online Education in India Retrieved from http://ijesc.org/upload/4e9a4612244093f84c7b9826de3f1d36.Impact%20of%20Online%20Education%20in%20Indian.pdf

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1579167-2019-08-09

15-16

5.

Authors: Partha Sarathi Bairy, Prashant Gahtori, Abhilasha Mishra, Veerma Ram

Paper Title: Ligand Based Pharmacophore Modeling and Virtual Screening for Novel Antidiabetics Targeting

PPAR-gamma

Abstract: A modern sedentary lifestyle with a more calorigenic fast-food diet increasing the prevalence of

metabolic syndrome in middle and high-income countries. Peroxisome proliferator-activated receptors (PPARs)

are a group of the nuclear receptor, which regulates the metabolic process in physiological systems via

influencing gene expressions of cell proliferation, glucose, lipid metabolism, and inflammation. Later one

PPAR-γ agonist is a well-established class of pharmacological agents for diabetic control with some promising

molecule in the clinical stages. Herein, we have chosen a hybrid indole and azaindole class for developing an

effective pharmacophore model. A series of compounds with indole carboxylic acid and hydroxyazaindole core

along with their tested biological activity were selected for generating a valid pharmacophore model using Hip-

Hop and HypoGen algorithm of Discovery Studio v3.1. A total of 38 numbers of ligand were considered for

pharmacophore generation and mapping including test set and training set. Depending upon proper calculative

measures the best-validated hypothesis with two hydrophobic, one hydrogen bond acceptor, and one ring

aromatic features are set forth for further shortlisting of compounds. A similarity search tool in PubChem

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structure database with a 70% similarity of best active compounds yields more than four lakhs compounds. The

screened drug-like compounds were further shortlisted using 'rules of five' and TOPKAT module. The best-

validated HypoGen pharmacophore was utilized for further screening to get the best structures for future in-

silico consideration and identifying potential hits for effective diabetes drug discovery research.

Keywords: PPAR-γ, diabetes mellitus, pharmacophore, ligand, metabolism.

References:

1. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: estimates for the year 2000 and projections

for 2030,” Diabetes Care, vol. 27, no. 5, pp. 1047–1053, 2004.

2. S. Bibi, and K. Sakata, “Current Status of Computer-Aided Drug Design for Type 2 Diabetes,” Curr Comput-Aid Drug Des, vol. 12, no.2, pp. 167-177, 2016.

3. WHO. WHO Library Cataloguing-in-Publication Data, Global Report on Diabetes. Geneva: World Health Organization; 2016.

[Online], Available: https://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf; jsessionid= 7D825C6497E92FBED545A4DE5DA7A0A2? sequence=1.

4. P.S. Bairy, B. Shankar, and A. Das, “Diabetes mellitus and antidiabetics with reference to alpha glucosidase inhibitors,” J Biomed

Pharm Res, vol. 4, no. 6, pp. 10-16, 2015. 5. P.S. Bairy, A. Das, L.M. Nainwal, T.K. Mohanta, M.K. Kumawat, P.K. Mohapatra, et al. “Design, synthesis and anti-diabetic

activity of some novel xanthone derivatives targeting α-glucosidase,” Bangladesh J Pharmacol, vol. 11, no. 2, pp. 308-318, 2016.

6. S.K. Garg, D. Giordano, T. Gallo, and V.N. Shah, “New Medications for the Treatment of Diabetes,” Diabetes Technol Ther, vol. 19, suppl. 1, pp. s128-s140, 2017.

7. P.S. Bairy, “Peroxisome proliferator-activated receptor-gamma, an emerging potential target to combat metabolic disorder,”

Asian J Pharm Clin Res, vol. 10, no. 12, pp. 40-44, 2017.

8. R.K. Petersen, K.B. Christensen, A.N. Assimopoulou, X. Frette, V.P. Papageorgiou, K. Kristiansen, et. al., “Pharmacophore-

driven identification of PPARγ agonists from natural sources,’ J Comput Aided Mol Des, vol. 25, no. 2, pp. 107–116, 2011.

9. A. Vuorinen, R. Engeli, A. Meyer, F. Bachmann, U.J. Griesser, D. Schuster, et. al., "Ligand-Based Pharmacophore Modeling and Virtual Screening for the Discovery of Novel 17β-Hydroxysteroid Dehydrogenase2 Inhibitors," J Med Chem, vol. 57, no. 14, pp.

5995−6007, 2014.

10. C.H. Lin, Y.H. Peng, M.S. Coumar, S.K. Chittimalla, C-C. Liao, P-C. Lyn, et al., “Design and Structural Analysis of Novel Pharmacophores for Potent and Selective Peroxisome Proliferator-activated Receptor γ Agonists,” J Med Chem, vol. 52, no. 8,

pp. 2618–2622, 2009.

11. J.F. Dropinski, T. Akiyama, M. Einstein, B. Habulihaz, T. Doebber, J.P. Berger, et al. “Synthesis and biological activities of novel arylindole-2-carboxylic acid analogs as PPAR γ partial agonists,” Bioorg Med Chem Lett, vol. 15, no. 22, pp. 5035–5038,

2005.

12. D. Schuster, C. Laggner, T.M. Steindl, A. Palusczak, R.W. Hartmann, and T. Langer, “Pharmacophore modeling and in silico screening for new P450 19 (Aromatase) inhibitors,” J Chem Inf Model, vol.46, no. 3, pp. 1301–1311, 2006.

13. J. Che, Z. Wang, H. Sheng, F. Huang, X. Dong, Y. Hu, et. al, “Ligand-based pharmacophore model for the discovery of novel CXCR2 antagonists as anti-cancer metastatic agents,” R Soc Open Sci, vol. 5, no. 7, pp. 180176, 2018.

14. S. Pal, V. Kumar, B. Kundu, D. Bhattacharya, N. Preethy, M.P. Reddy, et. al., “Ligand-based Pharmacophore Modeling, Virtual

Screening and Molecular Docking Studies for Discovery of Potential Topoisomerase I Inhibitors,” Comput Struct Biotechnol J, vol. 17, pp. 291–310, 2019.

15. N. Kaushik, N. Kaushik, P. Attri, N. Kumar, C.H. Kim, A.K. Verma, et. al., “Biomedical Importance of Indoles,” Molecules, vol.

18, no. 6, pp. 6620-6662, 2013.

16. B.R. Henke, K.K. Adkison, S.G. Blanchard, L.M. Leesnitzer, R.A. Mook Jr, K.D. Plunket, et. al., “Synthesis and biological

activity of a novel series of indole-derived PPARγ agonists,” Bioorg Med Chem Lett, vol. 9, no. 23, pp. 3329-3334, 1999.

17. P. Gahtori, R. Pandey, V. Kumar, S.K. Ghosh, A. Das, J.M. Kalita, et al. Toward resistance-compromised DHFR inhibitors part 1: Combined structure/ligand-based virtual screenings and ADME‐Tox profiling, J Chemometrics. Vol. 30, no. 8, pp.462-481,

2016.

18. H. Li, J. Sutter, and R. Hoffmann, “HypoGen: an automated system for generating predictive 3D pharmacophore models,” in Pharmacophore Perception, Development and Use in DrugDesign, 1st ed., International University Line, La Jolla, Calif, USA, pp.

173–189, 2000.

19. J. Fei, L. Zhou, T. Liu, and X-Y. Tang, "Pharmacophore Modeling, Virtual Screening, and Molecular Docking Studies for Discovery of Novel Akt2 Inhibitors," Int J Med Sci, vol. 10, no. 3, pp. 265-275, 2013.

20. Y. Wang, J. Xiao, T.O. Suzek, J. Zhang, J. Wang, and S.H. Bryant, “PubChem: a public information system for analyzing

bioactivities of small molecules,” Nucleic Acids Res, vol. 37, no. 2, pp. W623–W633, 2009. 21. T. Kaserer, V. Obermoser, A. Weninger, R. Gust, and D. Schuster, “Evaluation of selected 3D virtual screening tools for the

prospective identification of peroxisome proliferator-activated receptor (PPAR) γ partial agonists,” Eur J Med Chem, vol. 124,

pp. 49-62, 2016. 22. M.A. de Brito, “Pharmacokinetic study with computational tools in the medicinal chemistry course,” Braz J Pharm Sci, vol. 47,

no. 4, pp. 797-805, 2011.

6.

Authors: Jyoti Chhabra, Madhulika

Paper Title: Assessment of Antimicrobial Property of Naturally Coloured Cotton in Relation to Conventional

White Cotton

Abstract: Today’s consumer is looking for both physical and emotional well-being. In recent years,

enhancement of performance properties and imparting special properties to fabrics has become essential. One

such smart technology for textiles is introduction of anti-microbial finishes which impart feeling of freshness

and cleanliness to wearer. The micro-organisms that grow and thrive in warm, moist recesses of our clothing

cause foul smell and morbidity. To make the garments suitable for intimate apparel and children’s clothing, it is

important to make them antimicrobial by applying dyes or selected finishes. Some common natural dyes have

been found to exhibit antimicrobial property due to presence of large amounts of tannins. Tannins have the

property to bind the microbial proteins, thus inhibiting their growth. As naturally coloured brown cotton has

tannin derivatives and heavy metal ions as an integral part of its structure, it was considered imperative to

explore and compare the antimicrobial property of coloured cotton with conventional white cotton. The present

study was an endeavour in this direction where effort was made to explore the inherent antimicrobial property of

naturally coloured cotton. The antimicrobial property of conventional white and naturally coloured cotton was

assessed through Optical density and Standard Plate Count Test. The analysis of variance (ANOVA) highlighted

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that naturally coloured cotton significantly resisted the growth of microbes S. aureus, B. subtilis, E. coli and C.

albicans.Amongst the coloured cottons, brown sample gave higher resistance due to the presence of catechin and

other derivatives. The conidia and hyphae of fungus was hydrolysed due to catechin attack on the cell

membrane. It also resisted bacterial growth by damaging the bacterial membrane. Gram positive bacteria

exhibited better resistance because of the bactericidal effect of tannins present in naturally coloured cottons.

Keywords: cotton, naturally coloured cotton, antimicrobial, S.aureus, E.coli, C. albicans, B. subtilis, catechin,

fungus, tannins, performance properties, bacteria, fungus.

References:

1. “AATCC Technical Manual, (2009), Vol.84, American Association of Textiles chemists and Colorists.

2. Akiyama, H., Fujii, K., Yamasaki, O., Oono, T. and Iwatsuki, K., (2001), “Antibacterial Action of Several Tannins against Staphylococcus aureus”, Journal of Antimicrobial Chemotherapy, (48): 487-491.

3. Cappuccino, J. G. and Sherman, N., (1992), Microbiology- A Laboratory Manual (3rd Edition), The Benjamin/Cummings

Publishing Inc., California 94025. 4. Chaudhari, R., (2003), “Antimicrobials: A Potent Marketing Tool”, The Indian Textile Journal, November: 75-76.

5. Goyal, R., (2005), “Know Bacteria and Manage Your Textiles”, Colourage, June: 81-85.

6. Gupta, D. and Bhaumik S., (2007), “Antimicrobial Treatments for Textiles”, Indian Journal of Fibre and Textile Research, Vol. 32, June: 254-263.

7. Gupta, D. and Laha, A., (2007), “Antimicrobial Activity of Cotton Fabric Treated with Quercus infectoria Extract”, Indian

Journal of Fibre and Textile Research,Vol. 32, March: 88-92. 8. Gupta, D., Jain, A. and Panwar, S., (2005), “Anti-UV and Anti-microbial Properties of Some Natural Dyes on Cotton”, Indian

Journal of Fibre and Textile Research, Vol. 30, June: 190-195.

9. Gupta, S. P., (1996), “F Test and Analysis of Variance”, Statistical Methods, Revised Edition 1995, Reprint 1996, ISBN 81-7014-007-2, Sultan Chand and Sons, New Delhi.

10. Jhunjhunwala, B., (2008), “Sampling- Analysis of Variance- F-Test”, Business Statistics- A Self Study Textbook (I Edition), ISBN

81-219-2948-2, S. Chand and Company Ltd., New Delhi. 11. Kumar, G. R. and Krishnaveni, V., (2007), “Herbal Antimicrobial Finish on Cotton Fabric using Aloe Barbadensis Miller (Aloe

Vera)”, Asian Textile Journal, February:76-78.

12. Kut, D., Orhan, M., Gunesoglu, C. and Ozakin, C., (2005), “Effects of Environmental Conditions on the Antibacterial Activity of Treated Cotton Knits”, www.AATCC.org, March: 25-28

13. Masatomo, H. and Kazuko, T., (2004), “Multiple Effects of Green Tea Catechin on the Antifungal Activity of Antimycotics

against Candida albicans”, Journal of Antimicrobial Chemotherapy,53: 225–229. 14. Minium, E. D., King, B. M. and Bear, G., (2001), “One-Way Analysis of Variance (and Some Alternatives)”, Statistical

Reasoning in Psychology and Education (III Edition), ISBN 9971-51-171-1, John Wiley and Sons Inc., Singapore: 392-395. 15. Murray, P. R., Drew, W. L., George, S. K. and Thompson J. H., (1990), “Staphylococcus”, Medical Microbiology, The C.V.

Mosby Company, Missouri, USA: 47-63.

16. Orhan, M., Kut, D. and Gunesoglu, C., (2007), “Use of Triclosan as Antibacterial Agent in Textiles”, Indian Journal of Fibre and Textile Research, Vol. 32, Mach: 114-118.

17. Prescot, L. M., Harley, J. P. and Klein, D. A., (2003), Microbiology (III Edition), ISBN 0-07-122936-1, WCB McGraw- Hill,

USA. 18. Roy Choudhury, A. K., (2008), “Anti-Odour Finishing of Textiles”,Colourage, January: 88-96.

19. Sampath, V. R., (2003), “Functional Garments”, The Indian Textile Journal, January: 51-53.

20. Saravanan, D., (2005), “Antimicrobial Finishing of Textile Materials”, The Indian Textile Journal, October: 41-46. 21. Scarlbet, A., (1991), “Antimicrobial Properties of Tannins”, Photochemistry, Vol. 30(12): 3875-3883.

22. Shah, A. and Khanna, G., (2006), “Antimicrobial Finishing: Some FAQs”, Colourage, May: 98-99.

23. Tsutomu, Y., (2003), “Antimicrobial Effect of Catechin”, Modern Medical Laboratory, Vol. 31, No. 8: 761-763, www.sciencelinks.jp

24. Fox, Alvin, Microbiology and Immunology Online, pathmicro.med.sc.edu/book/intro-sta.htm

7.

Authors: Manika Manwal, Amit Gupta, Sonali Gupta, Shiv Ashish Dhondiyal

Paper Title: Hadoop and Big Data Framework: A Technological Comparison of Various Techniques and Tools

Abstract: BIG DATA is the impactful terms which we hear nowadays but the question arise what it is? So BIG

DATA is considered as the data which is rapidly generating huge amount of data but the question arises from

where does this colossal amount of data is being generated? The answer is that there is not only one source of

data generation but multiple sources are there of colossal data generation like social media e.ginstagram,

facebook etc. Big data is featured with three V’s and big data can be classified into data source, content format,

data stores,data staging and Data processing. This paper specifies the number of technologies which can be used

in Big Data Analysis and discussion liesaround the Hadoop, itscharacteristics, and the technologiesused by

Hadoop. This study specifies the comparison of all these techniques and helps the researchers to choose better

techniques that can be used to data analysis.

Keywords: Hadoop, Big Data, Map Reduce, PIG, YARN, HBase Sqoop, HDFS

References:

1. Manwal, M., & Gupta, A. (2017, November). Big data and hadoop—A technological survey. In 2017 International Conference on

Emerging Trends in Computing and Communication Technologies (ICETCCT) (pp. 1-6). IEEE.

2. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.

3. Qian, J., Lv, P., Yue, X., Liu, C., & Jing, Z. (2015). Hierarchical attribute reduction algorithms for big data using MapReduce.

Knowledge-Based Systems, 73, 18-31. 4. Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National science review, 1(2), 293-314.

5. Cadersaib, B. Z., Sta, H. B., &Rahimbux, B. A. G. (2018, October). Making an Interoperability approach between ERP and Big

Data context. In 2018 Sixth International Conference on Enterprise Systems (ES) (pp. 146-153). IEEE.

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6. Batista, G. E., Prati, R. C., &Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6(1), 20-29.

7. M. Beyer, D. Laney, The importance of big data: A definition, ID: G00235055, Retrieved from Gartner database [Online;

accessed December 2013], 2012, http://www.gartner.com/id=2057415. 8. Dhole Poonam B, GunjalBaisa L, “Survey Paper on Traditional Hadoop and Pipelined MapReduce”,InternationalJournal of

Computational Engineering Research Vol 03, Issue12

9. Vera-Baquero, A., Palacios, R. C., Stantchev, V., &Molloy, O. (2015). Leveraging big-data for business process analytics. The Learning Organization.

10. Chu, C. T., Kim, S. K., Lin, Y. A., Yu, Y., Bradski, G., Olukotun, K., & Ng, A. Y. (2007). Map-reduce for machine learning on

multicore. In Advances in neural information processing systems (pp. 281-288). 11. Thusoo, A., Sarma, J. S., Jain, N., Shao, Z., Chakka, P., Zhang, N., ... & Murthy, R. (2010, March). Hive-a petabyte scale data

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12. Singh, D., & Reddy, C. K. (2015). A survey on platforms for big data analytics. Journal of big data, 2(1), 8. 13. Suman Arora, Dr. Madhu Goel, “Survey Paper on Scheduling in Hadoop”, International Journal of Advance Research in

Computer Science and Software Engineering Volume 4, Issue 5,May2014.

14. Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information sciences, 275, 314-347.

15. O’Driscoll, A., Daugelaite, J., & Sleator, R. D. (2013). ‘Big data’, Hadoop and cloud computing ingenomics. Journal of

biomedical informatics, 46(5), 774-781. 16. Apache Pig. Attainedfromhttp://pig.apache.org.

17. Apache Hive. Attainedfromhttp://hive.apache.org.

18. Zhou, F., Pham, H., Yue, J., Zou, H., & Yu, W. (2015, August). Sfmapreduce: An optimized mapreduce framework for small files. In 2015 IEEE International Conference on Networking, Architecture and Storage (NAS) (pp. 23-32). IEEE.

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20. Bressoud, T. C., & Tang, Q. (2016, September). Results of a model for hadoop yarn mapreduce tasks. In 2016 IEEE International Conference on Cluster Computing (CLUSTER) (pp. 443-446). IEEE.

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8.

Authors: Singh. N, Kumar. V, Dhyani. A, Lall .S, Nawaz. A, Nardev Singh

Paper Title: Health Concern Associated To Mustartd Oil

Abstract: Mustard oil is consumed regularly in our diet through cooking. A number of researches endorse that

this oil contains several elements beneficial for human consumption. The good fat in this oil brings down the

chances of heart disease additionally decreases the bad cholesterol level and improve the good cholesterol level

in human body. It enhances the functionality of spleen and liver and subsequently the digestive system. Mustard

oil contains a high proportion of omega-3 fatty acids. Omega-3 fatty acids are the compounds beneficial in

relieving arthritis pain and ease stiffness of the joint as it acts as an anti-inflammatory agent. Despite several

health benefits this oil has been observed to be undesirable for human use and the excess of which may invite

several unavoidable hazards to human health.

Keywords: digestive system, human consumption.

References:

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Protection Division, Food and Agriculture Organization of the United Nations, Vialedelle Terme di Caracalla, 00153 Rome, Italy. 2. John L. (1984), the Herb book. 1st Ed. Bantan books, New York, U.S.A PP. 960.

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6. West, L., Tsui, I., Balch, B., Mayer, K. and Huth, P. J., (2002). Determination and health implication of the erucic acid content of broccoli florets, sprouts, and seeds. Journal of Food Science, 67 (7): 2641.

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01.04.2011).

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mustard),TheorAppl Genet., 122(6):1091-103.

32. Tickoo S., Singh H. B., BhattacharyyaD. K, Quality, Nutrition & Processing: Processing Technology, Enzymatic transesterification of Brassica juncea seed oil for production of neutraceuticals,221

33. Lammerink J &Morice M. I, (1971). Breeding biennial rapeseed (Brassica napus L.) with low content of erucic acid, New

Zealand, Journal of Agricultural Research, 14(4): 752-760. 34. Saini N, Singh N, Kumar A, Vihan N, Yadav S, Vasudev S, (2016). Development and validation of functional CAPS markers for

the FAE genes in Brassica juncea and their use in marker-assisted selection, Breeding Science, 66: 831-837.

35. Verma A, Sharma A, Rai P K, (2019).Effect of microwave pre-treatment on quality parameters in Indian mustard, J Food Sci Technol, 56(11) : 4956-4965.

36. Maheshwari P. N, Stanley D.W, and Gray J. I. (1981). Detoxification of Rapeseed Products, Journal of Food Protection, Vol. 44

(6) :459-470.

9.

Authors: Navin Garg, Amit Gupta, D Bordoloi

Paper Title: Impact of Artificial Intelligence in Healthcare

Abstract: Artificial Intelligence (AI) is the trending technology that is affecting almost every aspect of our

lives. It is also contributed gradually to the changing field of medical sciences. Due to the enormous increase and

upgradation in the digital data acquisition, machine learning, deep learning, artificial intelligence applications

are too expanding in the areas which were previously only be handled by human experts only. In this review

paper we would like to specify the trending technologies using artificial intelligence with their benefits in

various biomedical applications. We further tried to identify the challenges of these AI systems in medical field.

Because of rapid changes in technology, healthcare sector will see drastic changes in how we prevent, diagnose

and cure disease.

Keywords:

References:

1. Panetta K. Top Trends in the Gartner Hype Cycle for Emerging Technologies, 2017. Gartner 2017:1–5.

2. Gartner Inc. Gartner Hype Cycle. Reproduction 2011;9:240–52. 271 doi:10.1016/j.accinf.2008.09.001.

3. Purdy M, Daugherty P. Why AI is the Future of Growth. 2016. doi:10.1016/j.techfore.2016.08.019.

4. Schmidhuber J. Deep Learning in neural networks: An overview. Neural Networks 2015;61:85–117. doi:10.1016/j.neunet.2014.09.003.

5. D. Curtis, E. Shih, J. Waterman, J. Guttag, J. Bailey, T. Stair, R. A. Greenes, and L. Ohno-Machado, ‘‘Physiological signal

monitoring in the waiting areas of an emergency room,’’ in Proc. ICST 3rd Int. Conf. Body Area Netw., Brussels, Belgium, 2008, pp. 5:1–5:8

47-50

10.

Authors: Noor Mohd, Annapurna Singh, H.S. Bhadauria, Ankur Dumka, Indrajeet Kumar

Paper Title: Cloud Computing Based Intrusion Detection System Challenges and Method

Abstract: Before few years the cloud computing innovation has at last come of age. The internet computing

technology is changing fast as we know about it. The chances of cloud computing and possibilities are

unbounded; unhappily, so too are the thread and possibilities of unkind intrusions. So, it is very significant that

the procedures of a security related system are defined so as to stop prevention illegal access to data center and

data resources. Finally preventing opening of security currently comes out impractical goal. The evidence in

back of intrusion detection systems is not yet to deploying a single group of agents to investigate network traffic

but show for the patterns of network type attacks known is required. This paper is about the challenges and

methods in the intrusion detection system in cloud computing as we know it.

Keywords: Cloud Security, Cloud Computing, Intrusion Detection System, and IDS Security.

References:

1. Casas, Pedro, Johan Mazel, and Philippe Owezarski. "Unsupervised network intrusion detection systems: Detecting the unknown

without knowledge." Computer Communications 35, no. 7 (2012): 772-783.

2. DasGupta, Dipankar. Artificial immune systems and their applications. Springer Publishing Company, Incorporated, 2014.

3. Subashini, Subashini, and V. Kavitha. "A survey on security issues in service delivery models of cloud computing." Journal of network and computer applications 34, no. 1 (2011): 1-11.

4. Vieira, Kleber, Alexandre Schulter, Carlos Westphall, and Carla Westphall. "Intrusion detection for grid and cloud computing." It

Professional 4 (2009): 38-43.

5. Bakshi, Aman, and B. Yogesh. "Securing cloud from ddos attacks using intrusion detection system in virtual machine." In

Communication Software and Networks, 2010. ICCSN'10. Second International Conference on, pp. 260-264. IEEE, 2010.

51-56

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6. Nikolai, J.; Yong Wang,"Hypervisor-based cloud intrusion detection system",IEEE,Computing, Networking and

Communications (ICNC), 2014 International Conference on,2014

7. Sarbazi-Azad, H.; Zomaya, A.,"Addressing Open Issues on Performance Evaluation in Cloud Computing",Wiley-IEEE

Press,Large Scale Network-Centric Distributed Systems,2014

8. Alsharafat, W.S.,"Proposed anticipating learning classifier system for cloud intrusion detection (ALCS-CID)",IEEE,Systems and

Informatics (ICSAI), 2014 2nd International Conference on,2014

9. Kholidy, H.A.; Erradi, A.; Abdelwahed, S.; Azab, A.,"A Finite State Hidden Markov Model for Predicting Multistage Attacks in Cloud Systems",IEEE,Dependable, Autonomic and Secure Computing (DASC), 2014 IEEE 12th International Conference

on,2014

10. Gupta, M.; Gao, J.; Aggarwal, C.; Han, J.,"Outlier Detection for Temporal Data",Morgan & Claypool,Outlier Detection for

Temporal Data,2014

11. Khattak, S.; Ramay, N.R.; Khan, K.R.; Syed, A.A.; Khayam, S.A.,"A Taxonomy of Botnet Behavior, Detection, and

Defense",IEEE,Communications Surveys & Tutorials, IEEE,2014

12. Sarbazi-Azad, H.; Zomaya, A.,"Market-Oriented Cloud Computing and The Cloudbus Toolkit",Wiley-IEEE Press,Large Scale

Network-Centric Distributed Systems,2014

13. Weiming Hu; Jun Gao; Yanguo Wang; Ou Wu; Maybank, S.,"Online Adaboost-Based Parameterized Methods for Dynamic

Distributed Network Intrusion Detection",IEEE,Cybernetics, IEEE Transactions on,2014

14. Butun, I.; Morgera, S.D.; Sankar, R.,"A Survey of Intrusion Detection Systems in Wireless Sensor

Networks",IEEE,Communications Surveys & Tutorials, IEEE,2014

15. Kaur, R.; Singh, M.,"A Survey on Zero-Day Polymorphic Worm Detection Techniques",IEEE, Communications Surveys &

Tutorials, IEEE,2014.

16. Mohd, N., Singh, A., & Bhadauria, H. S. Bioinspired Immune System for Intrusions Detection System in Self Configurable

Networks, International Journal of Advanced Computer Science and Applications(IJACSA),2019, pages 159-166,Volume

10,Issue10.

17. Mohd, N., Singh, A. & Bhadauria, H.S. A Novel SVM Based IDS for Distributed Denial of Sleep Strike in Wireless Sensor

Networks. Wireless Pers Commun 111, 1999–2022 (2020). https://doi.org/10.1007/s11277-019-06969-9.

18. Mohd, N., Singh, A. & Bhadauria, H.S. Taxonomy on Security Attacks on Self Configurable Networks International Journal of

Electronics and Information Engineering, Vol.3, No.1, PP.44-52, Sept. 2015

11.

Authors: Atul Singh, Rajesh P Verma, Jasmeet Kalra

Paper Title: Performance of an Air Curtain in Residential Building and Methods to Analyze: A short Review

Abstract: The use of air curtain in residential or commercial building has been increased substantially since last

decades. The air curtain not only prevents air exchange between outside and inside environment of a building to

decrease heat load, but also prevents dust and insects to enter into the building. The performance of air curtain is

enhanced by minimizing infiltration rate of outside air. The researchers have emphasized on air curtain supply

speed and angle to control its function. Computational fluid dynamics (CFD) is suggested by almost all

researchers to analyze it at full scale, because it is very difficult to analyze experimentally. CFD coupled with

multizone approach is very effective to evaluate the performance of an air curtain accurately. There is abundant

literature on enhancement of air curtain performance for cold continents, like European countries, USA etc. The

performance of an air curtain is highly dependable on climate conditions. A rigorous analysis is required for

warmer regions where greater temperature difference exists, like India.

Keywords: Air curtain, infiltration, exfiltration, CFD

References:

1. Guylaine Desmarais,Dominique Deromeand Paul Fazio, “Mapping of Air Leakage in Exterior Wall Assemblies”, Journal of

THERMAL ENV.& BLDG.SCI., Vol.24—October 2000

2. Tomas Gil-Lopez Juan Castejon-Navas Miguel A.Galvez-Huerta Paul G. O’Donohoe, “Energetic, Environmental and Economic

Analysis of Climatic Separation by means of Air Curtains in Cold Storage Rooms”, http://dx.doi.org/doi:10.1016/j.enbuild.2014.01.026, ENB 4780

3. David A. Carlson, Jerome A. Hogsette, Daniel L. Kline, Chris D. Geden, Androbert K. Vandermeer, “Prevention of Mosquitoes

(Diptera: Culicidae) and House Flies (Diptera: Muscidae) from Entering Simulated Aircraft with Commercial Air Curtain Units”, J. Econ. Entomol. 99(1): 182Ð193 (2006)

4. Qi D, Goubran S, Wang L, Zmeureanu R, “Parametric Study of Air Curtain Door Aerodynamics Performance Based on

Experiments and Numerical Simulations”, Building and EnvironmentVolume 129, 1 February 2018, Pages 65-73 5. Buildings Energy Data Book, 2012

6. S.J. Emmerich, A.K. Persily, Energy Impacts of Infiltration and Ventilation in U.S. Office Buildings Using Multizone Airflow

Simulation, IAQ Energy 98. (1998) 191–203 7. Chang Shu, Liangzhu (Leon) Wang, Cheng Zhang, Dahai Qi, “Air curtain effectiveness rating based on aerodynamics”, Building

and Environment 169 (2020) 106582

8. Liangzhu (Leon) Wang, Zhipeng Zhong, “An approach to determine infiltration characteristics of building entrance equipped with air curtains”, Energy and Buildings 75 (2014) 312–320

9. J.J. Costa*, L.A. Oliveira, M.C.G. Silva, “Energy savings by aerodynamic sealing with a downward-blowing plane air curtain—

A numerical approach”, Energy and Buildings 38 (2006) 1182–1193 10. Chadi Younes, Caesar Abi Shdid and Girma Bitsuamlak, “Air infiltration through building envelopes: A review”, Journal of

Building Physics35(3) 267-302

11. Burns P and Deru M (2003) Infiltration and Natural Ventilation Model for Whole-Building Energy Simulation of Residential Buildings NREL/CP-550-33698. Midwest Research Institute, National Renewable Energy Laboratory, Cary, NC.

12. H.Giráldez, C.D.Pérez Segarra, I. Rodríguez, A.Oliva, “Improved semi-analytical method for air curtains prediction”, Energy and

Buildings 66 (2013) 258–266 13. H Cho, K Gowri,B Liu, “Energy Saving Impact of ASHRAE 90.1 Vestibule Requirements:Modeling of Air Infiltration through

Door Openings”, Pacific Northwest National Laboratory Richland, Washington 99352

14. Sherif Goubran, Dahai Qi, Liangzhu (Leon) Wang, “Assessing dynamic efficiency of air curtain in reducing whole building annual energy usage”, Build Simul(2017) 10: 497–507

15. J.C. Gonc ̧alves, J.J. Costa, A.R. Figueiredo, A.M.G. Lopes, “CFD modelling of aerodynamic sealing by vertical and horizontal air curtains”, Energy and Buildings 52 (2012) 153–160

16. Ashika Rai, Jining Sun, Savvas A Tassou, “Numerical investigation of the protective mechanisms of air curtain in a refrigerated

57-60

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truck during door openings” ,Ashika Rai et al. / Energy Procedia 161 (2019) 216–223 17. Sherif Goubran, Dahai Qi, Wael F. Saleh, Liangzhu (Leon) Wang, Radu Zmeureanu, “Experimental study on the flow

characteristics of air curtains at building entrances”, Building and Environment 105 (2016) 225-235

18. V. K. Titariya, A. C. Tiwari, “Parametric Investigation of the Air Curtain for Open Refrigerated Display Cabinets”, International Journal of Soft Computing and Engineering (IJSCE),Volume-2, Issue-3, July 2012

19. M. Deru, P. Burns, “Infiltration and Natural Ventilation Model for WholeBuilding EnergySimulation of Residential Buildings”,

National Renewable EnergyLaboratory(NREL) 20. Sherif Goubran, Dahai Qi, Wael F. Saleh, Liangzhu (Leon) Wang, Radu Zmeureanu, “Experimental study on theflow

characteristics of air curtains at building entrances”, Building and Environment 105 (2016) 225-235

21. A.M. Foster, M.J. Swain, R. Barrett, P. D’Agaro, L.P. Ketteringham, S.J. James, “Three-dimensional effects of an air curtain used to restrict cold room infiltration”, Applied Mathematical Modelling 31 (2007) 1109–1123

22. Senwen Yang, Hatem Alrawashdeh, Cheng Zhang, Dahai Qi, Liangzhu (Leon) Wang, Ted Stathopoulos, “Wind effects on air

curtain performance at building entrances”, Building and Environment 151 (2019) 75–87 23. D. Frank P.F. Linden, “The effects of an opposing buoyancy force on the performance of an air curtain in the doorway of a

building”, Energy &Buildings(2015), Energy and BuildingsVolume 96, 1 June 2015, Pages 20-29

24. Qingyan Chen, Kisup Lee, Sagnik Mazumdar, Stephane Poussou, Liangzhu Wang,Miao Wang, Zhao Zhang, “Ventilation performance prediction for buildings: Model assessment”, Building and Environment 45 (2010) 295–303

25. Haghighat H and Li H (2004) Building airflow movement – validation of three airflow models. Journal of Architectural Planning

Research 21(4): 331–349 26. J.E. Jaramillo, F.X. Trias, A. Gorobets, C.D. Pérez-Segarra, A. Oliva, “DNS and RANS modelling of a turbulent plane impinging

jet”, International Journal of Heat and Mass Transfer 55 (2012) 789–801

27. Zhao Zhang, Wei Zhang, Zhiqiang John Zhai& Qingyan Yan Chen, “Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environmentsby CFD: Part 1—Summary of Prevalent Turbulence Models”, Volume 13,

Number 6, HVAC&R Research

28. Zhao Zhang, Wei Zhang, Zhiqiang John Zhai& Qingyan Yan Chen, “Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD Part 2—Comparison with Experimental Data from Literature”,

Volume 13, Number 6, HVAC & R Research

29. M. Arun and E.G. Tulapurkara, “Computation of turbulent flow inside an enclosure with central partition”, Progress in Computational Fluid Dynamics, Vol. 5, No. 8, 2005

30. Emmerich, S.J. (1997) Use of Computational Fluid Dynamics to Analyse Indoor air Quality Issues, NISTIR 5997, National

Institutes of Standards and Technology USA 31. Qingyan Chen, “Ventilation performance prediction for buildings: A method overview and recent applications”, Building and

Environment 44 (2009) 848–858

12.

Authors: Gagan Bansal, Kartik Kaushik, Pankaj Negi, James Kunjwal

Paper Title: Liquid socking Ability of Hybrid Biocomposite Material Having Epoxy Resin Matrix and

Reinforcement of Chicken Feather Fiber

Abstract: Different materials have different water socking ability and that makes it a hydrophobic and

hydrophilic in nature. Biocomposite materials have become the need of current material development

technologies. The immense use and application of biocomposite material is helping the researchers to upgrade

the material’s composition as per need. In the current research, the life durability of chicken feather fiber based

hybrid biocomposite materials, having epoxy resin CY- 230 thermoset polymer matrix is characterized. The

prepared sample with 5wt% CFF and 3wt% Extracted Fish residue power (ERP), considered optimum

composition during mechanical characterization was tested n different atmospheric conditions for 3 days. The

conditions adopted are submerged in water, mustard oil, soda, milk and Lemon water. The weight readings and

geometrical measurement are taken in every 12 hours. The result obtained shows that the minimum weight

change of sample was the one immersed in water. The readings obtained shows that liquid socking ability

becomes almost constant after 2 days i.e. 48 hours. The absorbability analysis concludes that the difference in

the absorption rate and capacity depends on the viscosity of the immersing liquid.

Keywords: Extracted Fish residue Powder, Chicken Feather fiber, Characterization, Biocomposite, Composite

Materials, Absorbability,.

References:

1. G. Bansal, “Composite Fabrication and Characterization using Chicken Feather Fiber , Fish Residue Particulate and Epoxy Resin

Matrix Amalgamation,” vol. 5, no. March, pp. 90–96, 2018.

2. G. Bansal and A. Roy, “Rockwell Hardness and Izod Impact Characterization of Chicken Feather Fiber reinforced epoxy Composite,” no. March, pp. 97–100, 2018.

3. G. Bansal and V. K. Singh, “Flame Retardation Characterization of Chicken Feather Fiber and Extracted Residue Powder from Fish,” Int. J. Eng. Sci. Comput., vol. 6, no. 8, pp. 2579–2581, 2016.

4. G. Bansal, S. VK, P. PP, and S. Rastogi, “Water Absorption and Thickness Swelling Characterization of Chicken Feather Fiber

and Extracted Fish Residue Powder Filled Epoxy Based Hybrid Biocomposite Water Absorption and Thickness Swelling

Characterization of Chicken Feather Fiber and Extracted Fish,” Int. J. Waste Resour., vol. 6, no. 3, pp. 1–7, 2016, doi: 10.4172/2252-5211.1000237.

5. A. Dash and S. Tripathy, “Mechanical characteristics of chicken feather teak wood dust epoxy filled composite,” IOP Conf. Ser. Mater. Sci. Eng., vol. 377, no. 1, 2018, doi: 10.1088/1757-899X/377/1/012111.

6. S. Cheng, K. tak Lau, T. Liu, Y. Zhao, P. M. Lam, and Y. Yin, “Mechanical and thermal properties of chicken feather fiber/PLA green composites,” Compos. Part B Eng., vol. 40, no. 7, pp. 650–654, 2009, doi: 10.1016/j.compositesb.2009.04.011.

7. D. D. Belarmino et al., “Physical and Morphological Structure of Chicken Feathers (Keratin Biofiber) in Natural, Chemically and

Thermally Modified Forms,” Mater. Sci. Appl., vol. 03, no. 12, pp. 887–893, 2012, doi: 10.4236/msa.2012.312129.

61-64

13.

Authors: Rajesh Pant, Jasmeet Kalra, Pankaj Negi, Rajesh P Verma

Paper Title: Thermal Analysis of Cooling Fin for Electronics Circuit

Abstract: In this paper we have used second law of thermodynamics. Fourier law and Newton’s law of cooling,

fin equations is used to solve a variety of steady heat conduction problem. The Problem is solved 65-67

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computationally using ANSYS Software. Analytical results were obtained for cooling fin. Convection is found

to be along with all the boundaries of the assembly except the lower most, which is fixed to the steel block. The

film coefficient is taken to be 50 W/m2K and the Bulk (ambient) Temperature is taken as 293 K. The block will

be containing a copper/aluminium heating element that releases heat, totaling to 27.2 W. The other boundaries of

the steel block are insulated to prevent the loss of heat energy. Due to temperature difference heat will flow from

higher temperature to lower temperature. Fins were used to increase heat transfer rate. Result obtain were nodal

temperature distribution and maximum value temperature in the component. we will also see how temperature

varies with different thermal conductivity of material. With the help of MATLAB graph has been plotted to

make it more understandable.

Keywords: - fin (extended surface), ANSYS software, MATLAB software, convection, conductivity of material

References:

1. Kern, D. and A.D. Kraus, 1972. “Extended Surface Heat Transfer”, McGraw Hill New York.

2. Mansingh, V. and K. Hassur, 1993. “Thermal Analysis of a Pin Grid Array Packing Proceedings of the International Electronics

Packaging” Conference, 1: 410-419 3. Bar- Cohen, A. and M. Jelinek, 1985. “Optimum Arrays of Longitudinal Rectangular Fins in . Convection Heat Transfer” Heat

Transfer Engineering, 6: 68-78

4. Sasaki, S. and T. Kishimoto, 1986. “Optimal Structure for Microgroove Cooling Fin forHigh Power LSI Devices” Electronics Letters, 22(25): 1332-1334

5. Kraus, A.D., 1988. “Sixty-Five Years of Extended Surface Technology” 1922-1987, Appl. Mech. Rev., 41: 321-364.

6. Yeh. R.H. and M. Chang, 1995. “Optimum Longitudinal Convective Fin Arrays” Int Comm.

7. Heat Mass Transfer, 22(3): 445-460.

8. Knight, R.W., J.S. Goodling and D.J. Hall, 1991. “Optimal Thermal Design of Forced Convection Heat Sinks Analytical”. ASME

Journal of Electronic Packaging, 113: 313-321. 9. Knight, R.W., D.J. Hall, J.S. Goodling and R.C. Jaeger,1992. “Heat Sink Optimization ” IEEE Transaction on Components

Hybrids and manufacturingTechnology, 15(5): 832-842

10. Proulikakos, D. and A. Bejan, 1982. “Fin Geometry for Minimum Entropy Generation in Forced Convection” ASME J. Heat Transfer, 104: 616-623

11. Metrol, A., 1993.“Optimization of Extruded Type External Heat Sink for Mutichip Module” ASME Journal of Elctronic

Packaging, 115: 440-444. 12. Leung, C.W. and S.D. Probert, 1989. “Heat-Exchanger Performance : Effect of Orientation” Applied Energy, 33: 235-252.

13. Teetsra, P.M., M.M. Yovanovich, J.R. Culham and T.F. Lem czyk, 1999. “Analytical Forced Convection Modeling of Plate Fin

Heat Sinks”. Proc. 15th Annu. IEEE Semicon. Thermal Meas .Manag Symp., San Diago, Calif., pp: 34-41. 14. Bergles, A.E.2001 “The Implications and Challenges of Enhanced Heat Transfer for the Chemical Process Industries”. Chem Eng

Res Des, Vol. 79, 437–44 .

15. Kou, H.S., J.J. Lee and C.Y. Lai, 2003. “Thermal Analysis and Optimum Fin Length of a Heat Sink Heat Transfer Engineering”, 24(2): 18-29 .

16. Malekzadeh, P., Rahideh, H, and Karami, G. 2006“Optimization of Convective-Radiative Fins by Using Differential

Quadrature”, Element Method, Energy Conversion and Management, Vol. 47, 1505–14 . 17. Arslanturk, C. and Ozguc, A.F.2006 “Optimization of a Central- Heating Radiator”, Applied Energy, Vol. 83, 1190–1197 .

18. Shaeri, M.R. and Yaghoubi, M.. 2009“Thermal Enhancement from Heat Sinks by UsingPerforated Fins”, Energy Conversion and

Management”\, Vol. 50, 1264–1270 .

14.

Authors: Jasmeet Kalra, Rajesh Pant, Pankaj Negi, Vijay Kumar

Paper Title: CFD Simulation for Analyzing Velocity and Pressure Drop In Primary Duct of Air Preheater

Abstract: Air preheater performance in power plants plays a crucial role in increasing the thermal efficiency and

thereby provides a better utilization of waste heat from flue gases. But presence of higher velocity gradient and

poor pressure distribution among the flow passages results in boundary layer separation in the airpreheater duct

which increases turbulence of air hence lowers the thermal efficiency. This paper aims at studying different

velocity and pressure differences present in the primary duct and measure to counter them with the help of

computational fluid dynamics (CFD) analysis tool fluent 14.0.

Keywords:

References:

1. Staseik J.A., “Experimental studies of heat transfer and fluid flow across undulated heat exchanger surfaces”, Int. J. Heat

Transfer. Vol. 41 Nos. 6-7, 1998, pp. 899-914.

2. T. Skiepko, “Effect of reduction in seal clearances on leakages in a rotary heat exchanger”, Heat recovery system CHP 9 (6),

1989, pp. 553-559.

3. Rakesh Kumar & Sanjeev Jain, “Performance Evaluation of air pre heater at off design condition”, Dept of Mech. Engg., IIT,

New Delhi, pp.1-4.

4. “Steam Book”, The Babcock & Wilcox Company, 2006, pp.20-7.

5. Donald Kern, “Process Heat Transfer”, 2004 Tata McGraw-Hill Publication, pp. 701.

6. TeodorSkiepko, Ramesh K. Shah , “A comparison of rotary regenerator theory and experimental results for an air preheater for a

thermal power plant”, Rochester Institute of Technology, Rochester,USA.

7. T. Skiepko, “Effect of reduction in seal clearances on leakages in a rotary heat exchanger”, Heat recovery system CHP 9 (6)

(1989) 553-559.

8. Rakesh Kumar & Sanjeev Jain , Performance Evaluation of air pre heater at off design condition”,Dept of Mech Engg, IIT,New

Delhi For books:

9. “Steam Book”, 2006 The Babcock & Wilcox Company, pp.20-7.

10. Donald Q.Kern, “Process Heat Transfer”,2004 Tata McGraw-Hill Publication, pp. 701.

11. Rodney R. Gay, “Power Plant Performance Monitoring”,2004, pp. 433.

68-70

15. Authors: Vijay Kumar, Mohd. Shah, Jasmeet Kalra, Bhaskar Pant

Page 16: International Journal of Innovative Technology and ...€¦ · Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania,

Paper Title: Analytical Study on the Effects of Electromagnetic Waves on Human Beings

Abstract: All electrical and electronic devices radiate electromagnetic waves. These EM waves are categorized

into two groups, Ionizing, and non-Ionizing. In this manuscript, health effects due to radiation are studied. UG

and PG students are worked in physics, electrical and electronics labs. Magnetic and electric fields are generated

around us, when electromagnetic waves penetrate inside the body of students it may affect the organs. The

radiation which is produced by apparatus is low frequency and exposure of these types of radiation may because

of childhood leukemia, headache, stress, etc. Certain tissues/cells of the body absorb the energy-specific

absorption rate (SAR). After the permissible limit of SAR, the radiation becomes harmful. It is concluded that

some types of radiation may become harmful to the health of body tissues/cells.

Keywords:

References:

1. Cloude, Shane (1995). An Introduction to Electromagnetic Wave Propagation and Antennas. Springer Science and Business

Media. pp. 28–33. ISBN 978-0387915012.

2. Electromagnetic Waves and Human Health 3. By Feyyaz Ozdemir and Aysegul Kargi Submitted: October 9th 2010Reviewed: May 10th 2011Published: June 21st 2011 DOI:

10.5772/16343

4. Cleveland, Jr., Robert F.; Ulcek, Jerry L. (August 1999). Questions and Answers about Biological Effects and Potential Hazards of Radiofrequency Electromagnetic Fields (PDF) (4th ed.). Washington, D.C.: OET (Office of Engineering and Technology)

Federal Communications Commission. Retrieved 29 January 2019.

5. Electromagnetic Radiation and Human Health.

6. May 2015, DOI: 10.13140/RG.2.2.13195.28962, M M Zaman Tanim, Tampere University of Applied Sciences

7. Ved Parkash. Sharma Neelima R. Kumar 2010 Changes in honeybee behavior and Biology under the influence of cellphone

radiations. Current Science, 98 10 8. Possible effects of Electromagnetic Fields (EMF) on Human Health. 2010 Scientific Committee On Emerging And Newly

Identified Health Risks (SCENIHR) http://pages.prodigy.net/unohu/electro.htm

9. Cifra M. Fields J. Z. Farhadi A. 2010 Electromagnetic cellular interactions. Progress In Biophysics and Molecular Biology. 1 24 10. Guidelines On Limits Of Exposure To Static Magnetic Fields. In: International Commission On Non-Ionizing Radiation

Protection ICNIRP Guidelines Health Physics April 2009 96 4

11. Possible effects of Electromagnetic Fields (EMF) on Human Health, Publisher: European Commission DG SANCO, Mats-Olof Mattsson, Anders Ahlbom, Karolinska Institutet, Possible effects of Electromagnetic Fields (EMF) on Human Health. Scientific

Committee On Emerging And Newly Identified Health Risks (SCENIHR) 19 July 2006 MRI: Magnetic Resonance Imaging

12. Possible effects of Electromagnetic Fields (EMF) on Human Health. 2010 Scientific Committee On Emerging And Newly Identified Health Risks (SCENIHR)

13. Burr HS Northrop F.S.C. 1935 The electrodynamic theory of life. The Quarterly Review of Biology 10(3), 322 EOF -333. 14. "Electromagnetic fields and public health". Fact Sheet No. 322, June 2007. [World Health Organization], Accessed 7 February

2010.

15. Kheifets, L (2010). "Pooled analysis of recent studies on magnetic fields and childhood leukemia". Br J Cancer. 103 (7): 1128–1135. DOI:10.1038/sj.bjc.6605838. PMC 3039816. PMID 20877339.

16. Salvan, A; Ranucci, A; Lagorio, S; Magnani, C (2015). "Childhood Leukemia and 50 Hz Magnetic Fields: Findings from the

Italian SETIL Case-Control Study". Int J Environ Res Public Health. 12 (2): 2184–204. DOI:10.3390/ijerph120202184. PMC

4344719. PMID 25689995Scientific Committee on Emerging; Newly Identified Health Risks-SCENIHR (January 2009). "Health

Effects of Exposure to EMF" (PDF). Brussels: Directorate-General for Health & Consumers - European Commission: 4–5.

Retrieved 27 April 2010. 17. Clinical Biochemistry, Volume 46, Issues 1–2, January 2013, Pages 59-63, Long-term (up to 20 years) effects of 50-Hz magnetic

field exposure on the immune system and hematological parameters in healthy men, Author links open overlay

panelYvanTouitouaYasminaDjeridaneaJacquesLambrozobFrançoiseCamusaBrahimSelmaouiac 18. Numerous studies are currently undertaken to explain the possible health effects of weak, “non-thermal” radiofrequency

electromagnetic fields2).

19. IARC Monographs on the identification of carcinogenic hazards to humans. International Agency for Research on Cancer. WHO. https://monographs.iarc.fr/agents-classified-by-the-iarc/.

20. Scientific Committee on Emerging and Newly Identified Health Risks − SCENIHR9)

21. Mobile phone use and glioma risk: A systematic review and meta-analysis.,Yang M, Guo W, Yang C, Tang J, Huang Q, Feng S, Jiang A, Xu X, Jiang G, PLoS One. 2017; 12(5):e0175136.

22. Mobile phone use and risk of brain tumors: a systematic review of the association between study quality, source of funding, and

research outcomes.,Prasad M, Kathuria P, Nair P, Kumar A, Prasad K, Neurol Sci. 2017 May; 38(5):797-810. 23. Evaluation of Mobile Phone and Cordless Phone Use and Glioma Risk Using the Bradford Hill Viewpoints from 1965 on

Association or Causation.,Carlberg M, Hardell L, Biomed Res Int. 2017; 2017().

24. Probabilistic Multiple-Bias Modeling Applied to the Canadian Data From the Interphone Study of Mobile Phone Use and Risk of Glioma, Meningioma, Acoustic Neuroma, and Parotid Gland Tumors.,Momoi F, Siemiatycki J, McBride ML, Parent MÉ,

Richardson L, Bedard D, Platt R, Vrijheid M, Cardis E, Krewski D, Am J Epidemiol. 2017 Oct 1

25. Mobile phone use and risk for intracranial tumors and salivary gland tumors - A meta-analysis. Bortkiewicz A, Gadzicka E, Szymczak W, Int J Occup Med Environ Health. 2017 Feb 21

71-77

16.

Authors: Shipra Agarwal, Romil Negi, Shipra Gupta

Paper Title: Unraveling India: Setting up the Premises for a Healthy Economy

Abstract: Indian economy is trotting for the past several years. The country has been unable to catch a

momentum in its growth rate which has stopped India from experiencing a high growth trajectory period. India

needs to take some audacious measures to transform the economy from a developing to a developed one. This

paper emphasizes on the structural problems of Indian economy, understanding how crucial it is to address them

and negligence of which will cause hindrance in the economic prosperity of the country and suggests some

policy measures to the problems.

Keywords: GDP, Agriculture, Urban Development, Infrastructure, Public Private Partnership, Education,

Manufacturing, Investments.

78-87

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References:

1. World Bank. World Development Indicators (WDI).

2. Ministry of Statistics and Programme Implementation. (2019a). Estimates of Gross Domestic Product for the Second Quarter

(July-September) 2019-20.

3. Ministry of Statistics and Programme Implementation. (2019b). Quick Estimates of Index of Industrial Production and Use-Based

Index for the Month of October,2019.

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Fragmantation.

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20. Yang, L. (2015). China’s Growth Miracle: Past, Present and Future.

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26. Damodaran, H. (November 30, 2019) “This is India’s first ever slowdown at a time of political as well as macroeconomic

stability,” Indian Express.

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17.

Authors: Vikas Tripathi, Bhasker Pant, Vijay Kumar

Paper Title: CNN Based Framework for Sentiment Analysis of Tweets

Abstract: The project deals with the problem of visual updates on twitter; that differentiates tweets according to

the context in which they are exposed: good or bad. Twitter is an online small-scale platform for showcasing

different thoughts perceptions related to any area, news, information etc. It is an informal communication

framework that allows clients to compose brief information of 280 characters long. It's a quickly developing

assistance with more than 400 million enlisted clients of which 326 million individuals are dynamic and half of

them sign on twitter each day - creating almost 500 million tweets every day. Considering this huge measure of

spending we want to pick up the declaration of open assessment by examining the feelings communicated in the

tweets. Investigating general assessment is fundamental for any kind of business. firms trying to identify the

appropriate response of their items in the market, foreseeing political decisions and anticipating financial

occasions, for example, stock costs. The purpose of this project is to develop an effective working class for

accurate and automated segmentation of the tweet stream.

Keywords: CNN, Tweets, Sentiment analysis, Machine Learning.

References:

1. M. Grothaus. (2018, Oct. 25). Twitter’s Q3 earnings by the numbers [online]. Available:

https://www.fastcompany.com/90256723/twitters-q3-earnings-by-thenumbers 2. S. Aslam. (2019, Jan. 6). Twitter by The Numbers: Stats, Demographics and Fun Facts [online]. Available:

https://www.omnicoreagency.com/ twitter-statistics/

3. M. Grothaus. (2018, Oct. 25). Twitter’s Q3 earnings by the numbers [online]. Available: https://www.fastcompany.com/90256723/twitters- q3-earnings-by-thenumbers

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http://www.wildml.com/2015/11/ understanding-convolutionalneural-networks-for-nlp/

88-90

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6. D. Gupta. (2017, June. 29). Architecture of Convolutional Neural Network Demystified [online]. Available: 7. https://www.analyticsvidhya.com/blog/2017/0 6/architecture- of-conv olutional- neuralnetworks-simplified-demystified/

Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. "Sequence to sequence learning with neural networks." Advances in neural

information processing systems. 2014. 8. Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification.

18.

Authors: V. Choudhary, Jasmeet Kalra, Awanish Sharma, R. Aggarwal

Paper Title: Corrosion Examination in Peroxide Solutions under Acidic pH

Abstract: In current investigation, results of electrochemical as well as weight loss test conducted on steels in

acidic peroxide solutions have been discussed. Electrochemical polarization test E vs. time curve,

potentiodynamic as well as cyclic polarization curve were performed on stainless steels SS304L, SS316L,

SS2205 and 6% Mo in peroxide solutions having H2O2 , 500 and 1000 parts per million (ppm). The pH of these

solutions for electrochemical tests was kept 4 with chloride and without chloride. Chloride content varied from 0

to 1000 ppm. Weight loss test was also accomplished on above stainless steels in peroxide solutions with

chloride content varied from 0 to 1000 ppm at same pH. Alongside an E-pH illustration of peroxide-water

coordination were constructed. Electrochemical results of tested steels show soaring extent of localized

corrosion while it is less relentless in crate of solutions exclusive of Cl-. In general, It is accomplished that acidic

peroxides among/lacking Cl- are corrosive to steels to anecdotal amount furthermore their corrosives augments

with boost in H2O2 as well as Cl- substance. The optimal material for managing these media is recommended to

be SS 2205. Results of Weight loss test showed that SS 304L is found to observe high degree of uniform

corrosion hence cannot be suggested for handling these media. Duplex stainless steels therefore have to be used.

Both electrochemical and weight loss tests on stainless steel showed increased resistance against corrosion in

order of: SS304L< SS316L < SS2205~ 6% Mo.

Keywords: Peroxide, steels, corrosion, E-pH

References:

1. Stampella, R.S., Albani, O.A. and Ruiz, E.R.; Corros. Sci. Vol. 35, No. 1-4, 1993.

2. Bloom, R., Jr., Weeks, L.E.; and Rayleigh, C. W.; Corrosion 16 (1960) 164t.

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Canada, 1998.

9. Macdiarmid, J.A., and Reichert, D.L.; 1992 International Symposium on Corrosion in Pulp and Paper Industry, Atlanta, p. 99.

10. Rämö, J.; Hydrogen Peroxide-Metals-Chelating Agents; Interactions and Analytical Techniques: Ph.D. Thesis(VTT, Helsinki

University of Technology and University of Oulu, Finland) 2002.

11. Rämö, J., Saarinen, K.; and Sillanpää, M.; Werkstoffe und Korrosion 53 (2002) 898.

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alloyed; Avesta Sheffield AB, S-774, 80 Avesta Sweden,1993.

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91-95

19.

Authors:

Suspended

Paper Title:

96-100

20. Authors: Mohit Kumar Ojha, Priti Sharma, Rupa Khanna Malhotra, Oshin Parasar

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Paper Title: What Leads to a Successful Public-Private Partnership: Identifying Critical Success Factors

Abstract: Public-Private Partnership (PPP) is a buzz word across the globe and countries are adopting it as a

favorable mode for infrastructure delivery. India has adopted PPP in the late 1970’s but the real strides have

been taken only after the Economic Reforms of 1991. A number of PPP projects have been started and many of

them have completed as well; the number keeps on increasing since its inception. It makes us wonder about what

is so good with PPP as compared to other means and alternatives? In this regard, the present study tries to

identify reasons /objectives of entering into a PPP project and the underlying benefits out of it. Further study

also attempts to come up with Critical Success Factors for successful implementation and delivery of project.

Study is conceptual in nature based on review of extensive literature in the field of PPP and related issues.

Reports and case studies on different PPP project in national and international context has also been accessed to

gain an understanding of PPP projects, its elements, process and most importantly benefits, success factors and

reasons of failure. Websites of government agencies of different countries and other world bodies has also been

explored to understand government’s view point of PPP projects.

Keywords: Public-Private Partnership, Infrastructure, India, Critical Success Factors, Reasons of Failure.

References:

1. Ahmed, M. and Aziz, A. (2007). Successful delivery of public-private partnerships for infrastructure development. Journal of

Construction Engineering and Management, 133(12), 918-931.

2. Appuhami, R., Perera, S., and Perera, H. (2011). Management Controls in Public–Private Partnerships: An Analytical Framework. Australian Accounting Review, 21(1), 64-79.

3. Brinkerhoff D. W. and Brinkerhoff J. M. (2011), Public-Private Partnerships: Perspectives on Purposes, Publicness, and Good

Governance, Public Administration and Development, 31(2-14), 2-14. 4. Carroll, P and Steane, P. (2005), Public-private partnerships: sectoral perspectives, Public-Private Partnerships: Theory and

practice in international perspective, 36-56, Antony Rowe Ltd., Eastbourne

5. Cheung, E. and Chan, A. P. C. (2011). Evaluation model for assessing the suitability of Public-Private Partnership projects. Jiurnal of Management in Engineering, 27(2), 80-89.

6. Das, S. C. and Nandy, M. (2008), Public Private Partnership: an emerging issue, The ICFAI Journal of Infrastructure, VI(1), 18-

23. 7. Delmon J. (2011), Introduction, Public-Private Partnership Projects in Infrastructure: An Essential Guide for Policy Makers,

Cambridge University Press, New York, USA

8. Draft Compendium of PPP Projects in Infrastructure, (2012), PPP Infrastructure Division, retrieved 20th December, 2018, from www.infrastructure.gov.in, http://www.infrastructure.gov.in/pdf/Draft_Compendium_of_PPP_Projects_in_infrastructure.pdf

9. Estache, A., Juan, E., and Trujillo, L. (2007). Public-private partnerships in transport (Policy Research Working Paper No. 4436),

Retrieved from World Bank Website, https://openknowledge.worldbank.com/bitstream/handle/10986/7602/wps4436.pdf?sequence=1, doa: 28/03/2014

10. Grimsey, D and Lewis, M. K. (2007), Public Private Partnerships: The Worldwide Revolution in Infrastructure Provision and

Project Finance. Edward Elgar Publishing Limited: Northampton, MA 11. Gupta A. K. and Roy S. (2008), Public-Private Partnership in railways: a new approach, IIMB Management Review, 1-21

12. Hahn, D. (2010). How to create a public–private partnership: A replicable project associated with business continuity. Journal of

business continuity & emergency planning, 4(3), 274-285. 13. Hanss, W. G. (2001). Overcoming competitive disadvantages of public enterprises by Public–Private Partnerships and their

financing models. Annals of Public and Cooperative Economics, 72(3), 393-411.

14. Harishankar, K. S. and Sreeparvathy, G. (2013). Rethinking dispute resolution in public-private partnerships for infrastructure development in India. Journal of Infrastructure Development, 5(1), 21-32.

15. Herpen, G. W. E. B. V (2002). Public private partnerships, the advantages and disadvantages examined. Association of Europen

Transport, The Netherlands.

16. Jamali, D. (2004). Success and failure mechanisms of public private partnerships (PPPs) in developing countries: Insights from

the Lebanese context. International Journal of Public Sector Management, 17(5), 414-430.

17. Khan, M. S. (2014). A study of environmental constraints faced by public private partnership (PPP) in Indiaand the road to a

framework for successful implementation of PPP project. Business Review, 9(1), 14-32.

18. Koppenjan J. 2005. The formation of public-private partnerships: lessons from nine transport infrastructure projects in the

Netherlands. Public Administration, 83(1)

19. Kumar, K. (2008). Public-Private Partnership in Indian Railways (working paper no. 182), Retrieved from Centre for civil society

website, http://ccs.in/internship_papers/2007/Public-Private-Partnerhship-in-Indian-Railways-182.pdf, doa: 30/03/2014

20. Kwak, Y. H., Chih, Y., and Ibbs, C. W. (2009). Towards a comprehensive understanding of public private partnerships for

infrastructure development.California Management Review, 51(2), 51-78.

21. Lakshmanan, L. (Summer, 2008), Public-private partnership in Indian infrastructure development: issues and options, Reserve

Bank of India Occasional Papers, 29(1), 37-74.

22. Mahalingam, A. (2010). PPP experiences in Indian cities: barriers, enablers, and the way forward. Journal of Construction

Engineering and Management,136(4), 419-429.

23. Mishra A. K., Narendra K. and Kar B. P. (2013). Growth and infrastructure investment in India: Achivements challenges, and

opportunities. Economic Analysis, LVIII (196), 51-70.

24. Pillai, M. (2008). Infrastructure development and economic growth : The Public-Private Partnership (PPP) perspective. The Icfai

Journal of Infrastructure,6(1), 24-31.

25. Sachs, T., Tiong, R., and Wang, S. Q. (2007). Analysis of political risks and opportunities in public private partnerships (PPP) in

China and selected Asian countries: survey results. Chinese Management Studies, 1(2), 126-148.

26. Sharma, A. K. (2009), Modernization of railway stations in India: a case for public-private partnership with special reference to

New Delhi railway station, South Asian Journal of Management, 16 (1), 102-116.

27. Swain, S. C. (2009). Channelization of Potential Private Investment for Infrastructure Development in Orissa: A Public Private

Partnership Approach.IUP Journal of Infrastructure, 7, 32-55

28. Tasukada, S. (2013). Potential pitfalls in PPP application: lessons learned from National Highway Development Programmes in

India. Journal of Infrastructure Development, 5(1), 87-102.

29. Trailer, J. W., Rechner, P. L., and Hill, R. C. (2004). A compound agency problem: an empirical examination of public-private

partnerships. Journal of American Academy of Business, Cambridge, 5(1/2), 308-315.

30. Waring, J., Currie, G. and Bishop, S. (2013). A contingent approach to the organization and management of public-private

partnerships: an empirical study of English health care. Public Administration Review. 73(2), 313-326.

101-108

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31. Xu, Y., Skibniewski, M. J., Zhang, Y., Chan, A. P., and Yeung, J. F. (2012). Developing a concession pricing model for PPP

highway projects. International Journal of Strategic Property Management, 16(2), 201-217.

32. Zhang, X. (2005). Criteria for selecting the private-sector partner in public–private partnerships. Journal of construction

engineering and management, 131(6), 631-644.

33. Zhang, X. (2005). Critical success factors for public–private partnerships in infrastructure development. Journal of Construction

Engineering and Management, 131(1), 3-14.

Web Resources

34. A Guidebook on Public-Private Partnership in Infrastructure (2011, January). Retrieved on 12 th December, 2018 from http://www.unescap.org/ http://www.unescap.org/sites/default/files/ppp_guidebook.pdf

35. Asian Development Bank (2008). Public-Private Partnership (PPP) Handbook (2008, September). Retrieved 9th April, 2019, from

http://www.adb.org/: 36. http:/http://www.adb.org/sites/default/files/pub/2008/Public-Private-Partnership.pdf

37. Draft Compendium of PPP Projects in Infrastructure, (2012), PPP Infrastructure Division, retrieved 20 th December, 2018, from

www.infrastructure.gov.in, 38. http://www.infrastructure.gov.in/pdf/Draft_Compendium_of_PPP_Projects_in_infrastructure.pdf

39. Francoz, E. (2010, June). Advantages & Limitations of the Different Public Private Partnership Risks. Retrieved on 20 th

December, 2018, from http://www.afd.fr/ http://www.afd.fr/webdav/shared/PORTAILS/PAYS/MEDITERRANEE/PPP-Amman/AFD-Francoz.pdf

40. Katz, D. (2006, March). Financing Infrastructure Projects: Public Private Partnerships (PPPs). Retrieved on 20 th December, 2018,

from http://www.treasury.govt.nz/ http://www.treasury.govt.nz/publications/research-policy/ppp/2006/06-02/tpp06-02.pdf 41. CAG, New Zealand (2011). Managing the implications of public private partnerships (2011, November). Retrieved on 9 th April,

2019 from http://www.oag.govt.nz/ http://www.oag.govt.nz/2011/public-private-partnerships/docs/public-private-

partnerships.pdf 42. National Public Private Partnership Guidelines Volume 1: Procurement Options Analysis (2008, December). Retrieved 20 th

December, 2018, from http://www.infrastructureaustralia.gov.au/

http://www.infrastructureaustralia.gov.au/public_private/files/National_PPP_Guidelines_Volume_1_Procurement_Options_Anal

ysis_Dec_08.pdf

43. PPP Public Private Partnership Handbook (2013, October). Retrieved 12th December, 2014, from http://www.iddkarnataka.gov.in/

http://www.iddkarnataka.gov.in/docs/PPP_Handbook.pdf 44. Public Private Partnership Handbook for Uttarakhand A Working Guide (2010, November). Retrieved 12 th December, 2018, from

http://cell.upppc.org/ http://cell.upppc.org/index.php?option=com_content&view=article&id=32:ppp-

handbook&catid=12:upppc&Itemid=20 45. Ministry of Finance (2012). Public Private Partnership Handbook Version 2 (2012, March). Government of Singapore, Retrieved

20th December, 2018, from http://app.mof.gov.sg/ http://app.mof.gov.sg/data/cmsresource/ppp/PPPHandbook2012.pdf

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47. World Bank Institute (2012). Public-Private partnership: Reference Guide (Version 1), Retrieved 12th December, 2018, from http://www.ppiaf.org/ http://www.ppiaf.org/sites/ppiaf.org/files/publication/Public-Private-Partnerships-Reference-Guide.pdf

48. Resource Book on PPP Case Studies (2004, June). Retrieved 20th December, 2018, from http://ec.europa.eu/ http://ec.europa.eu/regional_policy/sources/docgener/guidespppresourcebook.pdf

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wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2010/01/11/000158349_20100111150559/Rendered/PDF/WPS5173.p

df

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